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6th International conference on Advanced computing and communication Systems (IcAccS)

作者： Grace, R. Kingsy Aishvarya, Karthika S. Monisha, B. Kaarthik, A. Sri Ramakrishna Engn Coll Dept Comp Sci & Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9781728151977;9781728151960

Since industrial revolution, the rate of industrialization and urbanization has increased dramatically. Most of the industry applications create pollution in the air and the vehicle emissions are also dangerous to the health of the people. In the developing countries, air pollution is severe in most of the areas. Air quality is the important factor to measure the quality of air. Most of the air quality measuring systems uses air quality index to tell the people about the air quality of their location. The primary objective of the system is to analyze and visualize air quality from the real time sensor data. The proposed system analyses six critical air pollutants which are, ozone (O-3), Particulate Matter (PM2.5), carbon monoxide (cO), Nitrogen dioxide (NO2) and Sulphur dioxide (SO2) are the most widespread health threats. The fuzzy c-means clustering is used to process the polluted air data from the sensors. From the results it is clear that the fuzzy c-means algorithm provides better results for the parameter accuracy while evaluating with the other algorithms in the literature.

关键词： Air Quality Index fuzzy c-means algorithm K-means algorithm

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Automated fault detection and classification of etch systems using modular neural networks

Automated fault detection and classification of etch systems...

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conference on Data Analysis and Modeling for Process control

作者： Hong, SJ May, GS Yamartino, J Skumanich, A Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

ISBN: (纸本)0819452912

Modular neural networks (MNNs) are investigated as a tool for modeling process behavior and fault detection and classification (FDc) using tool data in plasma etching. Principal component analysis (PcA) is initially employed to reduce the dimensionality of the voluminous multivariate tool data and to establish relationships between the acquired data and the process state. MNNs are subsequently used to identify anomalous process behavior. A gradient-based fuzzy c-means clustering algorithm is implemented to enhance MNN performance. MNNs for eleven individual steps of etch runs are trained with data acquired from baseline, control (acceptable), and perturbed (unacceptable) runs, and then tested with data not used for training. In the fault identification phase, a 0% of false alarm rate for the control runs is achieved.

关键词： advanced process control fault detection and classification principal component analysis modular neural networks fuzzy c-means algorithm

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Brain Tissue classification of Alzheimer Disease Using Partial Volume Possibilistic Modeling: Application to ADNI Phantom Images 7

Brain Tissue Classification of Alzheimer Disease Using Parti...

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7th International conference on Image Processing Theory, Tools and Applications (IPTA)

作者： Lazli, Lilia Boukadoum, Mounir Ait-Mohamed, Otmane ETS Dept Elect Engn Montreal PQ Canada Univ Quebec Montreal Dept Comp Sci CoFaM Res Ctr Montreal PQ Canada Concordia Univ Dept Elect Engn & Comp Sci Montreal PQ Canada

ISBN: (纸本)9781538618424

This paper describes an automatic segmentation approach for PET and T1-weighted MR images using a possibilistic clustering algorithm for deriving fuzzy tissue maps of white matter, gray matter and cerebrospinal fluid volumes, and using the fuzzy c-means algorithm for the centers initialization process;this hybrid technique allows to compute the degree of membership of each voxel to different brain tissues. The fuzzy process is illustrated for Alzheimer's disease using phantom images from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our method, inspired from the conventional possibilistic algorithm, is less sensitive to noise while taking into consideration the effect of partial volume.

关键词： Alzheimer disease Brain tissue clustering fuzzy c-means algorithm Possibilistic c-means algorithm ADNI database

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Distributed energy efficient clustering algorithm based on fuzzy logic approach applied for heterogeneous WSN 17

Distributed energy efficient clustering algorithm based on f...

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2nd International conference on computing and Wireless communication Systems (IccWcS)

作者： Mahboub, Aziz En-Naimi, El Mokhtar Arioua, Mounir Barkouk, Hamid Abdelmalek Essaadi Univ FST Tangier LIST Lab Dept Comp Sci Tangier Morocco Abdelmalek Essaadi Univ Natl Sch Appl Sci Team New Technol Trends Tetouan Morocco

ISBN: (纸本)9781450353069

Sensor networks are nowadays a popular wireless technology due their role in different applications. It becomes the fabric of our daily life, as these networks have the potential to enhance human life. In WSN major applications, sensor nodes have limited power resource and typically powered by small and limited batteries whish replacement is very difficult and expensive in hostile environment. To prolong the sensor network's lifetime, many routing protocols have been proposed to achieve the energy efficiency in homogenous and heterogeneous WSN networks. In this paper work, we proposed a new hybrid routing protocol based on fuzzy c-means and DEEc protocol to form clusters and manage the transmission of data to the base station. The simulation results demonstrate the performance and prove the effectiveness of the proposed protocol. The energy consumption is minimized and the network lifetime of the sensor nodes is extended.

关键词： Wireless sensor network Energy efficiency DEEc cluster Estimation fuzzy c-means algorithm

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Intrusion Detection and Risk Evaluation in Online Transactions Using Partitioning Methods 11th

Intrusion Detection and Risk Evaluation in Online Transactio...

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11th International conference on Multimedia and Network Information Systems (MISSI)

作者： Yazdani, Hossein choros, Kazimierz Wroclaw Univ Sci & Technol Fac Comp Sci & Management Wroclaw Poland

ISBN: (纸本)9783319986784;9783319986777

Security is the main issue for real time systems, specially for financial and banking systems. Some of the customers who pay much attention to confidentiality and security on their network activities and transactions prefer to use the most secure channels, and for the others speed and the ease of services are more important. An optimized method should be a solution, but both strategies follow one common idea that any anomaly, abnormality, and intrusion should be handled in advance, as the reputation of each organization is based on trust. This paper proposes a new method with the aim of considering any anomaly in advance, in addition to partitioning strategy. The BFPM method makes use of the well-known fuzzy c-means clustering algorithm to evaluate whether packets or transactions are risky or not, and in what extent they will be risky in the near future. The proposed method aims to provide a flexible search space to cover prevention and prediction techniques at the same time.

关键词： Network security Intrusion detection Online transaction Partitioning methods fuzzy c-means algorithm BFPM method clustering Risk evaluation Object movement

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Military Equipment Project Interim Evaluation Based on fuzzy Theory 18

Military Equipment Project Interim Evaluation Based on Fuzzy...

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International conference on Mathematics and Statistics (IcoMS)

作者： chen Xiaowei Zhang Junqi cui Kan Astronaut Engn Univ Beijing Peoples R China

ISBN: (纸本)9781450365383

Military equipment project interim evaluation has great significance to project management and control. Firstly, the index system is established from five aspects: development, organization and management, performance, prospective benefit and resources, which includes qualitative and quantitative indexes. Then an interim evaluation model is built to solve the hybrid multi-index evaluation problem: the values of qualitative and quantitative indexes are pretreated by fuzzy membership function;the indexes weight vectors are determined by AHP;the evaluation results are calculated by multilevel fuzzy comprehensive evaluation;the evaluation conclusions are analysed by fuzzy c-means algorithm, which avoids the disadvantage of information loss by the maximum membership degree method and information annihilation by weighted average method. Finally, the effectiveness and feasibility are verified by a case study.

关键词： Project interim evaluation Military equipment project Index system fuzzy comprehensive evaluation fuzzy c-means algorithm

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A fuzzy cluster algorithm Based on Mutative Scale chaos Optimization

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5th International Symposium on Neural Networks

作者： Li, chaoshun Zhou, Jianzhong Li, Qingqing Xiang, Xiuqiao Huazhong Univ Sci & Technol Coll Hydroelect Digitizat Engn Wuhan 430074 Peoples R China

ISBN: (纸本)9783540877332

The traditional fuzzy: c-means (FcM) algorithm has some disadvantages in optimization method, which makes the algorithm liable to fall into local optimum, thus failing to get the optimal clustering results. According to the defect of FcM algorithm, a new fuzzy clustering algorithm based on chaos Optimization (FccO) is proposed in this paper, which combines mutative scale, chaos optimization strategy acid gradient method together. Moreover, a fuzzy cluster validity index (PBMF) is introduced to make the FccO algorithm capable of clustering automatically. Three other fuzzy cluster validity indices, namely XB, Pc and PE, are utilized to compare the performances of FccO, MINI and another algorithm, when applied to artificial and real data sets classification. Experiment results show FccO algorithm is more likely to obtain the global optimum and achieve Better performances on validity indices than other algorithms.

关键词： clustering fuzzy c-means algorithm chaos optimization cluster validity indices

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Abnormal Wind Power Data Identification Based on the Improved FcM algorithm and considering the Influence of Wind Speed 9

Abnormal Wind Power Data Identification Based on the Improve...

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9th International conference on Power and Energy Systems (IcPES)

作者： Fang, chen Zhang, Zhiyu Su, Xiangjing Zeng, Ping Tian, Shuxin State Grid Shanghai Elect Power Res Inst Grid Technol Ctr Shanghai Peoples R China Shanghai Univ Elect Power Coll Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781728126586

In the big data era, the planning, operation and maintenance of power systems are increasingly dependent on the support of various power data. Wind turbine operating data is an important data set considering the increasing installation capacity worldwide. However, due to factors such as unscheduled shutdown, load shedding and communication noise, a great variety and amount of abnormal data may be present in wind power data, which harms the economic and safe operation of wind turbines. In some cases, abnormal operating status even cannot be detected in time due to the disruption of abnormal data, causing serious accidents. Therefore, it is necessary to identify the abnormal wind power data from massive measurements and ensure the availability of accurate and effective data. In this paper, an abnormal wind power data identification strategy is proposed by the improved fuzzy c-means (FcM) algorithm and considering the influence of wind speed. Specifically, the feasible domain matrix is employed to identify abnormal data while the mean comparison method is utilized for data correction. The feasibility and effectiveness of the proposed abnormal wind power data identification and correction strategies in this study is tested by detailed simulations on the East-china Sea offshore wind power ScADA database.

关键词： abnormal data identification fuzzy c-means algorithm wind power and data correction

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A cluster Number Specification-Free algorithm in Networks Intrusion Detection

A Cluster Number Specification-Free Algorithm in Networks In...

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7th World congress on Intelligent control and Automation

作者： Xiao, Lizhong Wu, Qingtao Shanghai Inst Technol Dept Comp Sci & Informat Engn Shanghai 200235 Peoples R China Henan Univ Sci & Technol Coll Elect & Informat Engn Luoyang Peoples R China

ISBN: (纸本)9781424421138

With the crucial problem of specifying cluster number in clustering algorithm, a cluster number specification-free algorithm, F-cMSVM, is proposed in this paper. Firstly, the data set is classified into two clusters by fuzzy c-means algorithm (FcM). Then the result is tested by Support Vector Machine (SVM) associated with a fuzzy membership function to confirm whether the data set could be classified. Finally, the process is repeated and the clustering result can be obtained. With this unsupervised algorithm, not only does the training data set need no labeling, but also the cluster number needs no specifying. Experiments over networks connection records from KDD cUP 1999 data set were implemented to evaluate the proposed method. To obtain an appropriate training data set and overcome the low efficiency in processing the high dimensional data seta a cross method and a feature selection algorithm based on mutual information were applied respectively in experiments. The result dearly shows the outstanding performance of the proposed method in decision of cluster number and effect of intrusion detection.

关键词： fuzzy c-means algorithm Support Vector Machine cluster number intrusion detection

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Improvement of cSF, WM and GM Tissue Segmentation by Hybrid fuzzy - Possibilistic clustering Model based on Genetic Optimization case Study on Brain Tissues of Patients with Alzheimer's Disease

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INTERNATIONAL JOURNAL OF NETWORKED AND DISTRIBUTED cOMPUTING 2018年第2期6卷 63-77页

作者： Lazli, Lilia Boukadoum, Mounir Univ Quebec UQAM Dept Comp Sci CoFaM Res Ctr Ste Foy PQ Canada Univ Quebec ETS Dept Elect Engn Ste Foy PQ Canada

Brain tissue segmentation is one of the most important parts of clinical diagnostic tools. fuzzy c-mean (FcM) is one of the most popular clustering based segmentation methods. However FcM does not robust against noise and artifacts such as partial volume effect (PVE) and inhomogeneity. In this paper, a new approach for robust brain tissue segmentation is described. The proposed method quantifies the volumes of white matter (WM), gray matter (GM) and cerebrospinal fluid(cSF) tissues using hybrid clustering process which based on: (1) FcM algorithm to get the initial center partition. (2) Genetic algorithms (GA) to achieve optimization and to determine the appropriate cluster centers and the fuzzy corresponding partition matrix. (3) Possibilistic c-means (PcM) algorithm for volumetric measurements of WM, GM, and cSF brain tissues. (4) Rule of the possibility maximum to compute the labeled image in decision step. The experiments were realized using different real and synthetic brain images from patients with Alzheimer's disease. We used Tanimoto coefficient, sensitivity and specificity validity indexes to validate the proposed hybrid approach and we compared the performance with several competing methods namely FcM and PcM algorithms. Good result was achieved which demonstrates the efficiency of proposed clustering approach and that it can outperforms competing methods especially in the presence of PVE and when the noise and spatial intensity inhomogeneity are high.

关键词： fuzzy c-means algorithm Possibilistic c-means algorithm Genetic algorithms Hybrid reasoning Brain tissue clustering Alzheimer's disease

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